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Zhang, X.Y.[Xiang-Yu],
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PAMI(37), No. 9, September 2015, pp. 1904-1916.
IEEE DOI
1508
BibRef
Earlier:
ECCV14(III: 346-361).
Springer DOI
1408
Accuracy
BibRef
He, K.M.[Kai-Ming],
Zhang, X.Y.[Xiang-Yu],
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Deep Residual Learning for Image Recognition,
CVPR16(770-778)
IEEE DOI
1612
Award, CVPR.
BibRef
And:
Identity Mappings in Deep Residual Networks,
ECCV16(IV: 630-645).
Springer DOI
1611
BibRef
Lee, C.Y.[Chen-Yu],
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Generalizing Pooling Functions in CNNs: Mixed, Gated, and Tree,
PAMI(40), No. 4, April 2018, pp. 863-875.
IEEE DOI
1804
computational complexity, feedforward neural nets,
learning (artificial intelligence), neural net architecture,
supervised classification
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Xu, C.Y.[Chun-Yan],
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UP-CNN: Un-pooling augmented convolutional neural network,
PRL(119), 2019, pp. 34-40.
Elsevier DOI
1902
Convolutional neural network, Cross-layer interaction,
Ratio un-pooling, Image classification
BibRef
Lai, X.[Xin],
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Second-Order Spectral Transform Block for 3D Shape Classification and
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IEEE DOI
2003
3D shape analysis, second-order pooling, spectral transform,
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Wang, S.D.[Shi-Dong],
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Multi-Granularity Canonical Appearance Pooling for Remote Sensing
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IP(29), 2020, pp. 5396-5407.
IEEE DOI
2004
Granular feature representation, transformation invariant,
Gaussian Covariance matrix, remote sensing scene classification
BibRef
Singh, P.[Pravendra],
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EDS pooling layer,
IVC(98), 2020, pp. 103923.
Elsevier DOI
2006
Feature pooling layer, Convolutional neural network,
Deep learning, Object recognition
BibRef
Xiao, B.[Bo],
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A novel Pooling Block for improving lightweight deep neural networks,
PRL(135), 2020, pp. 307-312.
Elsevier DOI
2006
Deep learning, Image classification, Object detection,
Pooling block, Lightweight neural networks
BibRef
Akodad, S.[Sara],
Bombrun, L.[Lionel],
Xia, J.[Junshi],
Berthoumieu, Y.[Yannick],
Germain, C.[Christian],
Ensemble Learning Approaches Based on Covariance Pooling of CNN
Features for High Resolution Remote Sensing Scene Classification,
RS(12), No. 20, 2020, pp. xx-yy.
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BibRef
Zhu, X.G.[Xiao-Guang],
Wang, H.Y.[Hao-Yu],
Liu, P.L.[Pei-Lin],
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Qian, J.C.[Jiu-Chao],
Graph-based reasoning attention pooling with curriculum design for
content-based image retrieval,
IVC(115), 2021, pp. 104289.
Elsevier DOI
2110
Content-based image retrieval, Graph convolutional networks, Curriculum design
BibRef
Gao, H.Y.[Hong-Yang],
Liu, Y.[Yi],
Ji, S.W.[Shui-Wang],
Topology-Aware Graph Pooling Networks,
PAMI(43), No. 12, December 2021, pp. 4512-4518.
IEEE DOI
2112
Network topology, Natural language processing,
Diversity reception, Training data, Sampling methods, graph topology
BibRef
Gao, Z.T.[Zi-Teng],
Wang, L.M.[Li-Min],
Wu, G.S.[Gang-Shan],
LIP: Local Importance-Based Pooling,
IJCV(131), No. 1, January 2023, pp. 363-384.
Springer DOI
2301
BibRef
Earlier:
ICCV19(3354-3363)
IEEE DOI
2004
convolutional neural nets, image classification,
importance sampling, learning (artificial intelligence),
Neural networks
BibRef
Xu, S.[Sixiang],
Muselet, D.[Damien],
Trémeau, A.[Alain],
Jiao, L.C.[Li-Cheng],
Improved Bilinear Pooling With Pseudo Square-Rooted Matrix,
SPLetters(30), 2023, pp. 423-427.
IEEE DOI
2305
Feature extraction, Symmetric matrices, Matrix decomposition,
Image classification, Convolutional neural networks,
Newton iterations
BibRef
Liu, Y.[Yue],
Cui, L.X.[Li-Xin],
Wang, Y.[Yue],
Bai, L.[Lu],
ABDPool: Attention-based Differentiable Pooling,
ICPR22(3021-3026)
IEEE DOI
2212
Aggregates, Benchmark testing, Graph neural networks,
Task analysis, Standards
BibRef
Grabinski, J.[Julia],
Jung, S.[Steffen],
Keuper, J.[Janis],
Keuper, M.[Margret],
FrequencyLowCut Pooling: Plug and Play Against Catastrophic Overfitting,
ECCV22(XIV:36-57).
Springer DOI
2211
BibRef
Akodad, S.[Sara],
Bombrun, L.[Lionel],
Puscasu, M.[Maria],
Xia, J.[Junshi],
Germain, C.[Christian],
Berthoumieu, Y.[Yannick],
Deep Ensemble Learning Model Based on Covariance Pooling of
Multi-Layer CNN Features,
ICIP22(1081-1085)
IEEE DOI
2211
Image recognition, Image analysis, Neural networks,
Convolutional neural networks, Remote sensing, Standards, CNN
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Chen, J.J.[Jia-Jing],
Kakillioglu, B.[Burak],
Ren, H.T.[Huan-Tao],
Velipasalar, S.[Senem],
Why Discard if You can Recycle?: A Recycling Max Pooling Module for
3D Point Cloud Analysis,
CVPR22(549-557)
IEEE DOI
2210
Point cloud compression, Representation learning, Solid modeling,
Semantics, Network architecture
BibRef
He, Y.J.[Yang-Ji],
Liang, W.H.[Wei-Han],
Zhao, D.Y.[Dong-Yang],
Zhou, H.Y.[Hong-Yu],
Ge, W.F.[Wei-Feng],
Yu, Y.Z.[Yi-Zhou],
Zhang, W.Q.[Wen-Qiang],
Attribute Surrogates Learning and Spectral Tokens Pooling in
Transformers for Few-shot Learning,
CVPR22(9109-9119)
IEEE DOI
2210
Visualization, Semantics, Self-supervised learning,
Benchmark testing, Transformers, Feature extraction, retrieval
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Stergiou, A.[Alexandros],
Poppe, R.[Ronald],
Kalliatakis, G.[Grigorios],
Refining activation downsampling with SoftPool,
ICCV21(10337-10346)
IEEE DOI
2203
Training, Image recognition, Refining, Memory management,
Object detection, Minimization, Feature extraction,
Recognition and classification
BibRef
Chen, J.C.[Jia-Cheng],
Hu, H.X.[He-Xiang],
Wu, H.[Hao],
Jiang, Y.N.[Yu-Ning],
Wang, C.H.[Chang-Hu],
Learning the Best Pooling Strategy for Visual Semantic Embedding,
CVPR21(15784-15793)
IEEE DOI
2111
Adaptation models, Visualization, Computational modeling,
Semantics, Benchmark testing, Feature extraction, Data models
BibRef
Hssayni, E.H.,
Ettaouil, M.,
A Novel Pooling Method for Regularization of Deep Neural networks,
ISCV20(1-6)
IEEE DOI
2011
convolutional neural nets, image classification,
learning (artificial intelligence), pattern classification,
Spectral Dropout.
BibRef
Douillard, A.[Arthur],
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Podnet: Pooled Outputs Distillation for Small-tasks Incremental
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ECCV20(XX:86-102).
Springer DOI
2011
BibRef
Chen, Z.,
Zhang, J.,
Ding, R.,
Marculescu, D.,
ViP: Virtual Pooling for Accelerating CNN-based Image Classification
and Object Detection,
WACV20(1169-1178)
IEEE DOI
2006
Convolution, Task analysis, Object detection, Interpolation,
Acceleration, Redundancy, Computational modeling
BibRef
Kobayashi, T.[Takumi],
Global Feature Guided Local Pooling,
ICCV19(3364-3373)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, learning (artificial intelligence),
Probabilistic logic
BibRef
Wan, W.,
Chen, J.,
Li, T.,
Huang, Y.,
Tian, J.,
Yu, C.,
Xue, Y.,
Information Entropy Based Feature Pooling for Convolutional Neural
Networks,
ICCV19(3404-3413)
IEEE DOI
2004
convolutional neural nets, entropy, feature extraction,
image classification, image segmentation,
Training
BibRef
Huang, J.,
Li, Z.,
Li, N.,
Liu, S.,
Li, G.,
AttPool: Towards Hierarchical Feature Representation in Graph
Convolutional Networks via Attention Mechanism,
ICCV19(6479-6488)
IEEE DOI
2004
convolutional neural nets, feature extraction, graph theory,
learning (artificial intelligence), pattern classification,
Adaptation models
BibRef
Gao, Z.L.[Zi-Lin],
Xie, J.T.[Jiang-Tao],
Wang, Q.L.[Qi-Long],
Li, P.H.[Pei-Hua],
Global Second-Order Pooling Convolutional Networks,
CVPR19(3019-3028).
IEEE DOI
2002
BibRef
Xu, Y.,
Nakayama, H.,
DCT Based Information-Preserving Pooling for Deep Neural Networks,
ICIP19(894-898)
IEEE DOI
1910
Deep neural network, information preservation, spectral pooling, 2D-DCT
BibRef
Hu, G.,
Dixit, C.,
Luong, D.,
Gao, Q.,
Cheng, L.,
Salience Guided Pooling in Deep Convolutional Networks,
ICIP19(360-364)
IEEE DOI
1910
Pooling, Salience feature, Classification, Edge
BibRef
Saeedan, F.[Faraz],
Weber, N.[Nicolas],
Goesele, M.[Michael],
Roth, S.[Stefan],
Detail-Preserving Pooling in Deep Networks,
CVPR18(9108-9116)
IEEE DOI
1812
Standards, Visualization, Convolutional neural networks,
Task analysis, Feature extraction, Distortion, Adaptive systems
BibRef
Ferrŕ, A.[Aina],
Aguilar, E.[Eduardo],
Radeva, P.[Petia],
Multiple Wavelet Pooling for CNNs,
WiCV-E18(IV:671-675).
Springer DOI
1905
BibRef
Ryu, J.B.[Jong-Bin],
Yang, M.H.[Ming-Hsuan],
Lim, J.W.[Jong-Woo],
DFT-based Transformation Invariant Pooling Layer for Visual
Classification,
ECCV18(XIV: 89-104).
Springer DOI
1810
DFT magnitude pooling replaces the traditional max/average pooling
layer between the convolution and fully-connected layers.
BibRef
Yu, K.C.[Kai-Cheng],
Salzmann, M.[Mathieu],
Statistically-Motivated Second-Order Pooling,
ECCV18(VII: 621-637).
Springer DOI
1810
Second-order network.
BibRef
Simon, M.[Marcel],
Gao, Y.[Yang],
Darrell, T.J.[Trevor J.],
Denzler, J.[Joachim],
Rodner, E.[Erik],
Generalized Orderless Pooling Performs Implicit Salient Matching,
ICCV17(4970-4979)
IEEE DOI
1802
CNN. Learn the pooling strategy also.
feature extraction, feedforward neural nets,
image classification, image matching, image representation,
Visualization
BibRef
Zhai, S.F.[Shuang-Fei],
Wu, H.[Hui],
Kumar, A.[Abhishek],
Cheng, Y.[Yu],
Lu, Y.X.[Yong-Xi],
Zhang, Z.F.[Zhong-Fei],
Feris, R.[Rogerio],
S3Pool: Pooling with Stochastic Spatial Sampling,
CVPR17(4003-4011)
IEEE DOI
1711
Convolution, Distortion, Feature extraction, Neural networks,
Standards, Stochastic, processes
BibRef
Cui, Y.,
Zhou, F.,
Wang, J.,
Liu, X.,
Lin, Y.,
Belongie, S.J.[Serge J.],
Kernel Pooling for Convolutional Neural Networks,
CVPR17(3049-3058)
IEEE DOI
1711
Kernel, Neural networks, Taylor series, Tensile stress, Training, Visualization
BibRef
Li, P.H.[Pei-Hua],
Xie, J.T.[Jiang-Tao],
Wang, Q.L.[Qi-Long],
Gao, Z.L.[Zi-Lin],
Towards Faster Training of Global Covariance Pooling Networks by
Iterative Matrix Square Root Normalization,
CVPR18(947-955)
IEEE DOI
1812
Covariance matrices, Graphics processing units, Training,
Matrix decomposition, Backpropagation, Symmetric matrices,
Computer architecture
BibRef
Chen, Z.Q.[Zi-Qian],
Lin, J.[Jie],
Chandrasekhar, V.[Vijay],
Duan, L.Y.[Ling-Yu],
Gated Square-Root Pooling for Image Instance Retrieval,
ICIP18(1982-1986)
IEEE DOI
1809
Logic gates, Principal component analysis, Training,
Benchmark testing, Image retrieval, Task analysis,
Learning to gate
BibRef
Xie, L.X.[Ling-Xi],
Tian, Q.[Qi],
Flynn, J.[John],
Wang, J.D.[Jing-Dong],
Yuille, A.L.[Alan L.],
Geometric Neural Phrase Pooling:
Modeling the Spatial Co-Occurrence of Neurons,
ECCV16(I: 645-661).
Springer DOI
1611
BibRef
Bell, S.[Sean],
Zitnick, C.L.[C. Lawrence],
Bala, K.[Kavita],
Girshick, R.[Ross],
Inside-Outside Net: Detecting Objects in Context with Skip Pooling
and Recurrent Neural Networks,
CVPR16(2874-2883)
IEEE DOI
1612
BibRef
Yang, F.,
Choi, W.,
Lin, Y.,
Exploit All the Layers: Fast and Accurate CNN Object Detector with
Scale Dependent Pooling and Cascaded Rejection Classifiers,
CVPR16(2129-2137)
IEEE DOI
1612
BibRef
Mopuri, K.R.[Konda Reddy],
Babu, R.V.[R. Venkatesh],
Object level deep feature pooling for compact image representation,
DeepLearn15(62-70)
IEEE DOI
1510
Computational modeling
BibRef
Yang, M.[Mu],
Li, B.[Brian],
Fan, H.Q.[Hao-Qiang],
Jiang, Y.N.[Yu-Ning],
Randomized spatial pooling in deep convolutional networks for scene
recognition,
ICIP15(402-406)
IEEE DOI
1512
deep convolutional networks
BibRef
Yoo, D.G.[Dong-Geun],
Park, S.G.[Sung-Gyun],
Lee, J.Y.[Joon-Young],
Kweon, I.S.[In So],
Multi-scale pyramid pooling for deep convolutional representation,
DeepLearn15(71-80)
IEEE DOI
1510
Accuracy
BibRef
Liu, L.Q.[Ling-Qiao],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
The treasure beneath convolutional layers:
Cross-convolutional-layer pooling for image classification,
CVPR15(4749-4757)
IEEE DOI
1510
DCNN.
BibRef
Gong, Y.C.[Yun-Chao],
Wang, L.W.[Li-Wei],
Guo, R.Q.[Rui-Qi],
Lazebnik, S.[Svetlana],
Multi-scale Orderless Pooling of Deep Convolutional Activation Features,
ECCV14(VII: 392-407).
Springer DOI
1408
Deep convolutional neural networks
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Efficient Implementations Convolutional Neural Networks .